Articles | Volume 16, issue 15
Development and technical paper
10 Aug 2023
Development and technical paper |  | 10 Aug 2023

Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model

Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik

Related authors

Downscaling precipitation over High Mountain Asia using Multi-Fidelity Gaussian Processes: Improved estimates from ERA5
Kenza Tazi, Andrew Orr, Javier Hernandez-González, Scott Hosking, and Richard E. Turner
EGUsphere,,, 2023
Short summary
Convolutional conditional neural processes for local climate downscaling
Anna Vaughan, Will Tebbutt, J. Scott Hosking, and Richard E. Turner
Geosci. Model Dev., 15, 251–268,,, 2022
Short summary
Polar stratospheric clouds initiated by mountain waves in a global chemistry–climate model: a missing piece in fully modelling polar stratospheric ozone depletion
Andrew Orr, J. Scott Hosking, Aymeric Delon, Lars Hoffmann, Reinhold Spang, Tracy Moffat-Griffin, James Keeble, Nathan Luke Abraham, and Peter Braesicke
Atmos. Chem. Phys., 20, 12483–12497,,, 2020
Short summary
Projecting ozone hole recovery using an ensemble of chemistry–climate models weighted by model performance and independence
Matt Amos, Paul J. Young, J. Scott Hosking, Jean-François Lamarque, N. Luke Abraham, Hideharu Akiyoshi, Alexander T. Archibald, Slimane Bekki, Makoto Deushi, Patrick Jöckel, Douglas Kinnison, Ole Kirner, Markus Kunze, Marion Marchand, David A. Plummer, David Saint-Martin, Kengo Sudo, Simone Tilmes, and Yousuke Yamashita
Atmos. Chem. Phys., 20, 9961–9977,,, 2020
Short summary
Progress towards a probabilistic Earth system model: examining the impact of stochasticity in the atmosphere and land component of EC-Earth v3.2
Kristian Strommen, Hannah M. Christensen, Dave MacLeod, Stephan Juricke, and Tim N. Palmer
Geosci. Model Dev., 12, 3099–3118,,, 2019
Short summary

Related subject area

Climate and Earth system modeling
An emulation-based approach for interrogating reactive transport models
Angus Fotherby, Harold J. Bradbury, Jennifer L. Druhan, and Alexandra V. Turchyn
Geosci. Model Dev., 16, 7059–7074,,, 2023
Short summary
A sub-grid parameterization scheme for topographic vertical motion in CAM5-SE
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873,,, 2023
Short summary
Technology to aid the analysis of large-volume multi-institute climate model output at a central analysis facility (PRIMAVERA Data Management Tool V2.10)
Jon Seddon, Ag Stephens, Matthew S. Mizielinski, Pier Luigi Vidale, and Malcolm J. Roberts
Geosci. Model Dev., 16, 6689–6700,,, 2023
Short summary
A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research
Maria-Theresia Pelz, Markus Schartau, Christopher J. Somes, Vanessa Lampe, and Thomas Slawig
Geosci. Model Dev., 16, 6609–6634,,, 2023
Short summary
Monte Carlo drift correction – quantifying the drift uncertainty of global climate models
Benjamin S. Grandey, Zhi Yang Koh, Dhrubajyoti Samanta, Benjamin P. Horton, Justin Dauwels, and Lock Yue Chew
Geosci. Model Dev., 16, 6593–6608,,, 2023
Short summary

Cited articles

Agarwal, N., Kondrashov, D., Dueben, P., Ryzhov, E., and Berloff, P.: A Comparison of Data-Driven Approaches to Build Low-Dimensional Ocean Models, J. Adv. Model. Earth Sy., 13, e2021MS002537,, 2021. a
Arcomano, T., Szunyogh, I., Wikner, A., Pathak, J., Hunt, B. R., and Ott, E.: A Hybrid Approach to Atmospheric Modeling That Combines Machine Learning With a Physics-Based Numerical Model, J. Adv. Model. Earth Sy., 14, e2021MS002712,, 2022. a, b
Arjovsky, M., Chintala, S., and Bottou, L.: Wasserstein generative adversarial networks, in: International conference on machine learning, PMLR, 214–223,, 2017. a
Arnold, H. M., Moroz, I. M., and Palmer, T. N.: Stochastic parametrizations and model uncertainty in the Lorenz’96 system, Philosophical Transactions of the Royal Society A: Mathematical, Phys. Eng. Sci., 371, 20110479,, 2013. a, b, c, d
Bahdanau, D., Cho, K., and Bengio, Y.: Neural machine translation by jointly learning to align and translate, arXiv [preprint],, 2014. a
Short summary
How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.